eISSN: 3087-4319 editor@aimjournals.com
All Journals
Submit your article

International Journal of Next-Generation Engineering and Technology

Open Access Peer Review International
Open Access

Architectural Design and System-Level Solutions for Seamless Incorporation of Robotic Technologies into Existing Industrial Infrastructure Networks

DOI
PDF
Dr. Muhammad Arif Hidayat 1 ,
4 Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Teknologi Nusantara Bandung 40132, Indonesia

Abstract

The integration of robotic technologies into existing industrial infrastructure networks has become a critical requirement for modern manufacturing and production ecosystems. However, heterogeneous legacy systems, limited interoperability, and scalability constraints continue to hinder seamless deployment. This paper presents a comprehensive survey-driven architectural framework that addresses system-level challenges in integrating robotic systems into established industrial environments. The study synthesizes recent advancements in cloud robotics, decentralized architectures, intelligent control systems, and modular manufacturing reconfiguration strategies. Particular emphasis is placed on evaluating scalable orchestration models and adaptive control mechanisms that support heterogeneous robotic platforms. Existing literature indicates that efficient integration requires not only hardware-software compatibility but also dynamic system coordination and optimized resource allocation strategies (Dawarka & Bekaroo, 2022). Furthermore, simulation-based approaches for determining optimal robotic system density highlight the importance of structural planning in modular production environments (Marschall et al., 2022). The proposed architectural model consolidates these insights into a layered system design incorporating perception, control, coordination, and cloud integration layers. The study also identifies key limitations in current frameworks, including latency issues, interoperability gaps, and insufficient real-time adaptability. The findings contribute toward establishing a scalable and standardized integration pathway for industrial robotics deployment in complex operational environments.

Keywords

Industrial Robotics Integration, System Architecture, Cloud Robotics

References

Dawarka V. Building and evaluating cloud robotic systems: a systematic review / V. Dawarka, G. Bekaroo // Robotics and Computer-Integrated Manufacturing. –2022. –Vol. 73.
Gómez J. Data-driven identification and control based on optic tracking feedback for robotic systems / J. Gómez, Ch. Treesatayapun, A. Morales // The International Journal of Advanced Manufacturing Technology. –2021. –Vol. 113. –No. 5. –Pp. 1485-1503.
Kota S.K. An evaluation of flying robotic systems using software controlled flight plans in its embedded systems / S.K. Kota, Sh.S. Thakur // Technoarete Transactions on Industrial Robotics and Automation Systems. –2022. –Vol. 2. –No. 4.
Marschall M. Defining the number of mobile robotic systems needed for reconfiguration of modular manufacturing systems via simulation / M. Marschall, M. Gregor, L. Ďurica, V. Vavrík, T. Bielik, P. Grznár, S. Mozol // Machines. –2022. –Vol. 10. –No. 5. –P. 316.
Polyakov R.Yu. Application of portable flying robotic systems for early detection of an ignition source / R.Yu. Polyakov // Periodico Tche Quimica. –2020. –Vol. 17. –No. 36. –Pp. 1082-1098.
Sharma R. Robotics System Integration Market Outlook / R. Sharma.
Siefke L. Robotic systems of systems based on a decentralized service-oriented architecture / L. Siefke, V. Sommer, B. Wudka, C. Thomas // Robotics. –2020. –Vol. 9. –No. 4. –Pp. 1-10.
Tavares P. Optimal automatic path planner and design for high redundancy robotic systems / P. Tavares, D. Marques, P. Malaca, G. Veiga, P. Costa, A.P. Moreira // Industrial Robot. –2020. –Vol. 47. –No. 1. –Pp. 131-139.
Vinogradov G.P. Patterns in intelligent control systems for robotic systems / G.P. Vinogradov // Studies in Systems, Decision and Control. –2021. –Vol. 352. –Pp. 15-28.
Yu Zh. Global composite learning fixed-time control of robotic systems with asymmetric tracking error constraints / Zh. Yu, K. Pang, Ch. Hua // IEEE Transactions on Industrial Electronics. –2023. –Vol. 70. –No. 7. –Pp. 7082-7091.

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

71-80 of 111

You may also start an advanced similarity search for this article.